Saturday, 24 December 2016

Applications of Predictive Analytics in various industries

They say that those who do not study history are doomed to repeat it. In no form of big data analysis is this phrase more relevant than in ‘predictive analytics’. In simple terms, predictive analytics is the systematic use of data, machine learning techniques and a host of statistical algorithms to identify patterns that forecast the likelihood of future outcomes based on huge chunks of historical data. It is all about keeping a keen eye on what has and is happening to determine the best possible assessment of what might happen in the near future.

Predictive analytics slightly differs from other forms of big data analytics in that it is the only form that gives futuristic forecasts. Others such as prescriptive analytics gives directions on what actions should be taken to remedy various corporate issues; diagnostic analytics determines what happened and shows us why while descriptive analytics tells us what is currently happening.

Why Predictive Analytics is Crucial in the Business World Today?

Where big money is concerned, strategical mistakes can cost companies millions of dollars in revenue and operational costs. To avoid this sort of loss, businesses need to invest in forecasting.

In that entire chain of occurrences, you can already see just how inefficient the system can be. Solving these issues is critical and that’s where learning data science comes in handy. Especially, analytics that can forecast and help in strategic decision-making.Without predictive analytics, how can you tell whether or not the product you come up with will be useful to the masses? Without professional data scientists, how would you know who is most likely to buy your product? Which marketing strategies are most likely to garner you the most market share?

Minimizing inefficiencies through predictive analytics

The truth is, the world has been running on an extremely inefficient system. It only takes you looking at the statistics to see just how true this is to date. Up to 90% of all start-up companies fail: marketing tactics include casting a wide net that more often than not does not yield favorable results (out of 80 cold calls made, you would be lucky to get 3 sales), and the list goes on.

The Marketing campaigns have now become more optimized and efficient. Long gone are the days of ‘spraying and praying’ all the while wasting valuable resources trying to capture an unsuitable market niche based on a “hunch”. Today, through specialized predictive analytics, companies across the board can formulate effective strategies to identify, attract and capture markets for their products and services. The dependency on “gut feeling” has reduced.

E-commerce websites like Amazon have been making use of predictive analytics to capture usage patterns and past search data of website visitors to recommend products. A quick look around their website, or for that matter any other e-commerce player, will make you realize how predictive analytics is working so well. Amazon offers choices based on your likes and incites you to buy those products. From insurance companies to real estate, and almost every retail company, predictive analytics is now very much part of every operation.

Fraud detection

One of the main reason as to why predictive analytics has come to the forefront of the business world now is because the digital penetration has increased incredibly. Through big data analysis, there are systems in place that can combine multiple analytics methods to detect fraudulent patterns that indicate criminal behavior.

Today, one of the major concerns in the world is cyber security. With everything going virtual, to protect itself and its clients, major industry players such as banks, hospitals, social media companies and even police stations have resorted to using predictive analytics to minimize network breaches that could expose valuable information. Through the use of analytical methods, companies can detect vulnerabilities within their systems as well as abnormalities that indicate fraud.

Big online financial providers like PayPal have long used predictive analytics to determine what kind of precautions they have to take to protect their clients against fraudulent users.

PayPal uses data such as your historical payment data, to the kind of device often used as well as your PayPal user profile and country of origin, all these go into building machine learning algorithms that detect potential signs of fraud with every transaction.

Law enforcement agencies such as police departments on the other hand, feed off a large pool of data to police and protect the general public. From past criminal records/databases, to incident reports, crime tips as well as citizen feedback and CI information, the police can keep an eye on known criminals as well as potential acts of crime.

Reduction of risk

This is probably one of the very first examples of predictive analytics in action that most of us interact with on a regular basis. Every adult knows why credit score matters.

From banking to real estate, insurance, and even telecommunication, to get any form of credit or service nowadays, you need to have good credit. Credit risk analysis is all about big data.

Computer systems take into account all your past financial dealings and history and use that data to determine whether or not you present a high lending risk. It predicts how you will behave should you be lent any money. Some systems also show whether or not you do pay back your debts despite being labelled as a high risk. It is all about historical data and how you manage any financial mishaps.

Apart from money lending, predictive analytics is put to use by big insurance companies such as Liberty Mutual to determine the policy holder’s life expectancy and thus premium values This is all based on survival models created to predict just how long you will most likely live based on your lifestyle choices and pre-existing conditions. That is why they ask you those entire medical and lifestyle based questions.

It improves operations

Today, most companies use predictive analysis to manage resources and forecast inventory. For examples, airlines and big travel industry providers such as Virgin Atlantic and ‘Amadeus’ use predictive analytics to set ticket prices based on the predicted volume of traveling customers.

Hotels use such systems to determine future occupancy rates to adjust accommodation prices. Similarly, most retailers use similar systems to determine what discounts can be given, when should those promotions be conducted and to figure out the expected ROI of the promotions, etc.

Conclusion

From Oil, Gas and Utilities to Retail and the Banking sector as well as manufacturing and health insurance, everyone is relying on various predictive analytics methods to improve how they run their businesses.

The amalgamation of Data science and Analytics has transcended almost every sector. applications of Predictive analytics are not only limited in innovatively supercharging business processes but also make the system more data-dependent than based on the gut feeling of the top management. Irrespective of whichever industry you belong to, if you look around the existing processes, you’ll find how predictive analytics helps in better decision making and if isn’t, then it’s time to make use of it.

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